Value of computed tomography texture analysis for prediction of perioperative complications during laparoscopic partial nephrectomy in patients with renal cell carcinoma

Georg Bier, Simone Bier, Malte Niklas Bongers, Ahmed Othman, Ulrike Ernemann and Johann-Martin Hempel

PLOS ONE, 2018, vol. 13, issue 4, 1-13

Abstract: Purpose: Tumorous texture is a marker for tumor tissue inhomogeneity. Based on this assumption, this study aims to evaluate the value of computed tomography texture analysis for imaging-based prediction of perioperative complications during laparoscopic partial tumor nephrectomy. Methods: A total of 106 patients with histologically confirmed renal cell carcinoma and pre-operative CT were included and volumetric texture analysis of the tumors was performed by two readers. Texture analysis parameter ratios and differences were calculated using the kidney parenchyma as reference (“reference-corrected”). Regression analysis was performed, regarding the value of the texture analysis parameters, for assessment of the tumor nuclear grade and the prediction of peri- and postoperative complications and approximated blood loss. Moreover, the inter-rater agreement in terms of the intra-class correlation coefficient (ICC) was calculated. Results: Regarding the reference-corrected values, the predictive value of texture analysis parameters for severe perioperative complications was highest for the standard deviation of the mean attenuation (Area under curve/AUC, .615; sensitivity, 93.8%, specificity, 30.0%), followed by the uniformity (AUC, .599; sensitivity, 62.5%, specificity, 60.0%), and the uniformity of distribution of positive pixels (AUC, .597; sensitivity, 62.5%; specificity, 61.1%). Conclusion: CT and CT texture analysis parameters are valuable for prediction of perioperative outcome before laparoscopic partial nephrectomy in patients with renal cell carcinoma.

Date: 2018
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0195270 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 95270&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0195270

DOI: 10.1371/journal.pone.0195270

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().